V. Fernao Pires

Bio: V. Fernao Pires is an academic researcher from INESC-ID. The author has contributed to research in topic(s): Inverter & Fault (power engineering). The author has an hindex of 18, co-authored 154 publication(s) receiving 1457 citation(s). Previous affiliations of V. Fernao Pires include Technical University of Lisbon & University of Lisbon.

Power converter interfaces for electrochemical energy storage systems – A review

Abstract: Energy storage concept that supports important technologies for electrical systems is well established and widely recognized. Several energy storage techniques are available, including an electrochemical energy storage system used to support electrical systems. These storage systems require interfaces based on power electronic converters for interconnection with an electrical system. This paper reviews the literature covering the various types of interfaces developed for electrochemical energy storage systems. Different electrochemical energy storage devices and their specificities regarding to integration with the electrical systems are described. . The various power converter interfaces that can be used for electrochemical energy storage systems are presented. These interfaces have been divided into standard, multilevel and multiport technology. The main characteristics and specificity of each topology considering its application to electrochemical energy storage systems are presented. The review also covers the smart storage concept and the requirements of the interface to integrate the electrochemical energy storage devices upon this concept.

Economic assessment of residential PV systems with self-consumption and storage in Portugal

Abstract: A progressive implementation of renewable microgeneration, mainly small sized Photovoltaics, in low voltage distribution networks is ongoing. In this context, self-consumption with storage allows to highlight the prosumer concept, bearing in mind that this strategy may be interesting both from a technical and economical perspectives. The intelligent network environment or Smart Grid comes close to this new model and may have a critical relevance in the management of intelligent power distribution networks, in the framework of a Smart Environment. This paper intends to give an additional contribution on the subject by investigating the economic profitability of different residential PV systems configurations. These include traditional “injects all into the network/consumes all from the network”, self-consumption, storage and net-metering. The joint operation of self-consumption and battery storage is particularly focused as it presents the current trend in residential PV systems. The results of the performed economic analysis point to the conclusion that self-consumption is already attractive, but storage is not a profitable solution, because battery investment is still too high, despite the cost reduction witnessed in recent years.

Quasi-Z-Source Inverter With a T-Type Converter in Normal and Failure Mode

Abstract: This paper presents a three-phase multilevel quasi-Z-source inverter (qZSI) topology operating in normal and fault-tolerant operation mode. This structure is composed by two symmetrical quasi-Z-source networks and a three-phase T-type inverter. Besides the intrinsic advantages of multilevel voltage source inverters, the proposed structure is also characterized by their semiconductor fault tolerance capability. This feature is only obtained through changes on the modulation scheme after the semiconductor fault and does not require additional extra-phase legs or collective switching states. In certain fault types, the reduction of the output power capacity will be compensated by the boost characteristic of the qZSI. The fault-tolerant behavior of the proposed topology is demonstrated by several simulation results of the converter in normal and fault condition. To validate the characteristics of this multilevel qZSI, an experimental prototype was also built to experimentally confirm the results.

Motor square current signature analysis for induction motor rotor diagnosis

Abstract: Induction machines play an important role in today’s industry. Thus, preventive maintenance combined with fault diagnosis techniques have become an essential issue. One of the most used techniques for the diagnosis of faults in the induction machine is motor current signature analysis (MCSA). This approach presents some limitations for induction motor rotor diagnosis, particularly for small faults. In this paper, a new motor square current signature analysis (MSCSA) fault diagnosis methodology is presented. The proposed technique is based on three main steps: first, the induction motor current is measured; secondly, the square of the current is computed; and finally a frequency analysis of the square current is performed. This technique allows more information to be obtained from a motor with a rotor fault than the classical MCSA approach. Simulation and experimental results are presented in order to confirm the theoretical assumptions. This methodology has also been tested for the identification of two distinct faults (broken bars and rotor eccentricity).

Dual-inverter for grid-connected photovoltaic system: Modeling and sliding mode control

Abstract: A fast and robust control strategy for a multilevel inverter in grid-connected photovoltaic system is presented. The multilevel inverter is based on a dual two-level inverter topology. There are two isolated PV generators that feeding each bridge inverter. The output of each inverter is connected to a three-phase transformer. The active and reactive powers flowing into the grid are controlled by a sliding mode algorithm. An alfa–beta space vector modulator is also used. The inverters DC voltages are also controller by a sliding mode controller. In this way, a fast and robust system controller is obtained. Several test results are presented in order to verify the effectiveness of the proposed system controller.

Multi-level conversion : high voltage choppers and voltage-source inverters

Abstract: The authors discuss high-voltage power conversion. Conventional series connection and three-level voltage source inverter techniques are reviewed and compared. A novel versatile multilevel commutation cell is introduced: it is shown that this topology is safer and more simple to control, and delivers purer output waveforms. The authors show how this technique can be applied to either choppers or voltage-source inverters and generalized to any number of switches.<>

Overview of Control Systems for the Operation of DFIGs in Wind Energy Applications

Abstract: Doubly fed induction generators (DFIGs), often organized in wind parks, are the most important generators used for variable-speed wind energy generation. This paper reviews the control systems for the operation of DFIGs and brushless DFIGs in wind energy applications. Control systems for stand-alone operation, connection to balanced or unbalanced grids, sensorless control, and frequency support from DFIGs and low-voltage ride-through issues are discussed.

Recent Advances in the Design, Modeling, and Control of Multiphase Machines—Part II

Abstract: The main objective of this two-part state-of-the-art paper called “Recent Advances in the Design, Modeling, and Control of Multiphase Machines” is to present latest contributions in the multiphase machines' field. The first part of this paper focuses on the recent progress in the design, modeling, and control, whereas the drive is in healthy operation. This second part presents relevant contributions in two not analyzed fields. The first is in relation with the use of the additional degrees of freedom of multiphase machines and the exploitation of their fault-tolerant capabilities without adding extra hardware. The second one analyzes multiphase generation, particularly in grid-connected wind energy conversion systems and stand-alone applications. Recent progresses are shown and open challenges and future research directions are discussed.

A sparse auto-encoder-based deep neural network approach for induction motor faults classification

Abstract: This paper presents a deep neural network (DNN) approach for induction motor fault diagnosis. The approach utilizes sparse auto-encoder (SAE) to learn features, which belongs to unsupervised feature learning that only requires unlabeled measurement data. With the help of the denoising coding, partial corruption is added into the input of the SAE to improve robustness of feature representation. Features learned from the SAE are then used to train a neural network classifier for identifying induction motor faults. In addition, to prevent overfitting during the training process, a recently developed regularization method called “dropout” which has been proved to be very effective in neural network was employed. An experiment performed on a machine fault simulator indicates that compared with traditional neural network, the SAE-based DNN can achieve superior performance for feature learning and classification in the field of induction motor fault diagnosis.